• Collaborative Research Project

• Review

• Static maps with ggmap

• Dynamic results presentation

  • Static website hosting with gh-pages

Purpose: Pose an interesting research question and try to answer it using data analysis and standard academic practices. Effectively communicate your results to a variety of audiences in a variety of formats.

Deadline:

• Presentation: In-class Friday 5 December

• Website/Paper: 12 December

The project is a 'dry run' for your thesis with multiple presentation outputs.

Presentation: 10 minutes maximum. Engagingly present your research question and key findings to a general academic audience (fellow students).

Paper: 6,000 words maximum. Standard academic paper, properly cited laying out your research question, literature review, data and methods, and findings.

Website: An engaging website designed to convey your research to a general audience.

As always, you should submit one GitHub repository with all of the materials needed to completely reproduce your data gathering, analysis, and presentation documents.

Note: Because you've had two assignments already to work on parts of the project, I expect high quality work.

• What is the data-ink ratio? Why is it important for effective plotting.

• What is visual weighting?

• Why should you avoid using the size of circles to mean anything in a plot?

• How many decimal places should you report in a table?

Last class we didn't have time to cover mapping with ggmap.

We've already seen how ggmap can be used to find latitude and longitude.

library(ggmap)

places <- c('Bavaria', 'Seoul', '6 Parisier Platz, Berlin',
            'Hertie School of Governance')

geocode(places)

##         lon      lat
## 1  11.49789 48.79045
## 2 126.97797 37.56654
## 3  13.37854 52.51701
## 4  13.38921 52.51286

qmap(location = 'Berlin', zoom = 15)

Example from: Kahle and Wickham (2013)

Use crime data set that comes with ggmap

names(crime)

##  [1] "time"     "date"     "hour"     "premise"  "offense"  "beat"    
##  [7] "block"    "street"   "type"     "suffix"   "number"   "month"   
## [13] "day"      "location" "address"  "lon"      "lat"

# find a reasonable spatial extent
qmap('houston', zoom = 13) # gglocator(2) see in RStudio

# only violent crimes
violent_crimes <- subset(crime,
    offense != "auto theft" & offense != "theft" &
    offense != "burglary")

# order violent crimes
violent_crimes$offense <- factor(violent_crimes$offense,
    levels = c("robbery", "aggravated assault", "rape", "murder"))

# restrict to downtown
violent_crimes <- subset(violent_crimes,
    -95.39681 <= lon & lon <= -95.34188 &
    29.73631 <= lat & lat <= 29.78400)

# Set up base map
HoustonMap <- qmap("houston", zoom = 14,  source = "stamen", maptype = "toner",
                legend = "topleft")

# Add points
FinalMap <- HoustonMap +
                geom_point(aes(x = lon, y = lat, colour = offense, 
                               size = 100),
                data = violent_crimes) +
                guides(size = FALSE,
                       colour = guide_legend(title = 'Offense'))

print(FinalMap)

When your output documents are in HTML, you can create interactive visualisations.

Potentially more engaging and could let users explore data on their own.

Big distinction:

Client Side: Plots are created on the user's (client's) computer. Often JavaScript in the browser. You simply send them static HTML/JavaScript needed for their browser to create the plots.

Server Side: Data manipulations and/or plots (e.g. with Shiny Server) are done on a server.

There are lots of free services (e.g. GitHub Pages) for hosting webpages for client side plot rendering.

You usually have to use a paid service for server side data manipulation plotting.

You already know how to create HTML documents with R Markdown.

There are a growing set of tools for interactive plotting:

• googleVis

• dygraphs

• rcharts

• ggvis

• networkD3

You can use the googleVis package to create Google plots from R.

Example from googleVis Vignettes.

# Create fake data
fake_compare <- data.frame(
                country = c("US", "GB", "BR"),
                val1 = c(10,13,14),
                val2 = c(23,12,32))

library(googleVis)
line_plot <- gvisLineChart(fake_compare)
print(line_plot, tag = 'chart')

## <!-- LineChart generated in R 3.1.1 by googleVis 0.5.6 package -->
## <!-- Sun Nov 16 11:30:52 2014 -->
## 
## 
## <!-- jsHeader -->
## <script type="text/javascript">
##  
## // jsData 
## function gvisDataLineChartID739d2a564224 () {
## var data = new google.visualization.DataTable();
## var datajson =
## [
##  [
##  "US",
## 10,
## 23 
## ],
## [
##  "GB",
## 13,
## 12 
## ],
## [
##  "BR",
## 14,
## 32 
## ] 
## ];
## data.addColumn('string','country');
## data.addColumn('number','val1');
## data.addColumn('number','val2');
## data.addRows(datajson);
## return(data);
## }
##  
## // jsDrawChart
## function drawChartLineChartID739d2a564224() {
## var data = gvisDataLineChartID739d2a564224();
## var options = {};
## options["allowHtml"] = true;
## 
## 
##     var chart = new google.visualization.LineChart(
##     document.getElementById('LineChartID739d2a564224')
##     );
##     chart.draw(data,options);
##     
## 
## }
##   
##  
## // jsDisplayChart
## (function() {
## var pkgs = window.__gvisPackages = window.__gvisPackages || [];
## var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || [];
## var chartid = "corechart";
##   
## // Manually see if chartid is in pkgs (not all browsers support Array.indexOf)
## var i, newPackage = true;
## for (i = 0; newPackage && i < pkgs.length; i++) {
## if (pkgs[i] === chartid)
## newPackage = false;
## }
## if (newPackage)
##   pkgs.push(chartid);
##   
## // Add the drawChart function to the global list of callbacks
## callbacks.push(drawChartLineChartID739d2a564224);
## })();
## function displayChartLineChartID739d2a564224() {
##   var pkgs = window.__gvisPackages = window.__gvisPackages || [];
##   var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || [];
##   window.clearTimeout(window.__gvisLoad);
##   // The timeout is set to 100 because otherwise the container div we are
##   // targeting might not be part of the document yet
##   window.__gvisLoad = setTimeout(function() {
##   var pkgCount = pkgs.length;
##   google.load("visualization", "1", { packages:pkgs, callback: function() {
##   if (pkgCount != pkgs.length) {
##   // Race condition where another setTimeout call snuck in after us; if
##   // that call added a package, we must not shift its callback
##   return;
## }
## while (callbacks.length > 0)
## callbacks.shift()();
## } });
## }, 100);
## }
##  
## // jsFooter
## </script>
##  
## <!-- jsChart -->  
## <script type="text/javascript" src="https://www.google.com/jsapi?callback=displayChartLineChartID739d2a564224"></script>
##  
## <!-- divChart -->
##   
## <div id="LineChartID739d2a564224" 
##   style="width: 500; height: automatic;">
## </div>

Note: Uses `results='asis' in the code chunk head.

To show the in R use plot instead of print and don't include tag = 'chart'.

library(WDI)
co2 <- WDI(indicator = 'EN.ATM.CO2E.PC', start = 2010, end = 2010)
co2 <- co2[, c('iso2c','EN.ATM.CO2E.PC')]

# Clean
names(co2) <- c('iso2c', 'CO2 Emmissions per Capita')
co2[, 2] <- round(log(co2[, 2]), digits = 2)

co2_map <- gvisGeoMap(co2, locationvar = 'iso2c',
                      numvar = 'CO2 Emmissions per Capita',
                      options = list(
                          colors = '[0xfff7bc, 0xfec44f,
                                    0xd95f0e]'
                          ))

Note: That 0x replaces # for hexadecimal colors.

CO2 Emmissions (metric tons per capita)

print(co2_map, tag = 'chart')

Note: you will need to view googleVis maps that are in R Markdown documents in your browser rather than RStudio's built in HTML viewer.

Use the dygraphs package to plot interactive time series using the dygraphs JavaScript library.

Not on CRAN yet, so install from GitHub

devtools::install_github(c('rstudio/htmltools',
                           'ramnathv/htmlwidgets',
                           'rstudio/dygraphs'))

library(dygraphs)
lungDeaths <- cbind(mdeaths, fdeaths)
dygraph(lungDeaths)

dygraph(lungDeaths) %>% dyRangeSelector()

library(networkD3)
data(MisLinks); data(MisNodes)
forceNetwork(Links = MisLinks, Nodes = MisNodes,
            Source = "source", Target = "target",
            Value = "value", NodeID = "name",
            Group = "group", opacity = 0.8)

Any file called index.html in a GitHub repository branch called gh-pages will be a hosted website.

The URL will be:

http://GITHUB_USER_NAME.github.io/REPO_NAME

Note: you can use a custom URL if you own one. See https://help.github.com/articles/setting-up-a-custom-domain-with-github-pages/

First create a new branch in your repository called gh-pages:

Then sync your branch with the local version of the repository.

Finally switch to the gh-pages branch.

You can use R Markdown to create the index.html page as before.

Simply place a new .Rmd file in the repository called index.Rmd and knit it to HTML. Then push it back up.

Your website will now be live.

Begin to create a website for your project with static and interactive graphics.

If relevant include:

• A table of key results

• A googleVis map

• A bar or line chart with googleVis or other

• A simulation plot created with Zelig showing key results from your regression analysis.